Ludwig

**Docs** · **Getting Started** · **Examples** · **Discord**
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What is Ludwig?
Ludwig is a **declarative deep learning framework** that lets you train, fine-tune, and deploy AI models — from LLM fine-tuning to tabular classification — using a YAML config file and zero boilerplate Python.
**Tech stack:** Python 3.12 · PyTorch 2.7+ · Pydantic 2 · Transformers 5 · Ray 2.54
Ludwig is hosted by the Linux Foundation AI & Data.
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What's New in Ludwig 0.16
| Feature | Description |
| ------------------------------- | ------------------------------------------------------------------------------------------------------ |
| **PatchTST & N-BEATS encoders** | State-of-the-art timeseries forecasting encoders with MASE/sMAPE metrics |
| **Advanced PEFT adapters** | PiSSA, EVA, CorDA/LoftQ initializers; TinyLoRA, OFT, HRA, WaveFT, LN-Tuning, VBLoRA, C3A adapter types |
| **VLM fine-tuning** | Train LLaVA, Qwen2-VL, InternVL via is_multimodal: true with gated cross-attention |
| **HyperNetwork combiner** | Conditioning-based feature fusion — one feature generates weights for others |
| **Nash-MTL & Pareto-MTL** | Game-theoretic and preference-based multi-task loss balancing |
| **LLM config generation** | ludwig generate_config "describe your task" — LLM writes the YAML for you |
| **ModelInspector** | Architecture analysis, weight collection, feature importance proxy |
| **Ray Serve & KServe** | Distributed and Kubernetes-native model deployment shims |
| **GRPO alignment** | Reward-model-free RLHF via Group Relative Policy Optimization |
| **torchao quantization + QAT** | PyTorch-native int4/int8/float8 with Quantization-Aware Training |
| **Multi-adapter PEFT** | Multiple named LoRA adapters with weighted merging (TIES, DARE, SVD) |
| **Native Optuna executor** | GPT/TPE/CMA-ES samplers, pruning, resumable SQLite/PostgreSQL storage |
| **Timeseries forecasting** | model.forecast(dataset, horizon=N) API with TimeseriesOutputFeature |
| **Muon & ScheduleFreeAdamW** | New optimizers for large-scale pretraining and fine-tuning |
| **Image segmentation decoders** | UNet, SegFormer, FPN decoders for semantic segmentation |
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Installation
Requires Python 3.12+. See contributing for a full dependency matrix.
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Quick Start
Fine-tune an LLM (instruction tuning)
Ludwig supports the full LLM fine-tuning spectrum:
| Technique | Config key |
| --------------------------------- | ------------------------------------------------------------------------ |
| Supervised fine-tuning (SFT) | trainer.type: finetune |
| DPO / KTO / ORPO / GRPO alignment | trainer.type: dpo (or kto, orpo, grpo) |
| LoRA / DoRA / VeRA / PiSSA | adapter.type: lora (or dora, vera, lora + init_weights: pissa) |
| 4-bit QLoRA (bitsandbytes) | quantization.bits: 4 |
| torchao + QAT | quantization.backend: torchao |
| Multi-adapter with merging | adapters: dict + merge: block |
| VLM (vision-language) | is_multimodal: true |
Train a multimodal classifier
Generate a config from natural language
Make predictions
Launch a REST API
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Capabilities
LLM Fine-Tuning
- **Supervised fine-tuning (SFT)** on instruction/response pairs
- **Alignment training**: DPO, KTO, ORPO, GRPO (reward-model-free RLHF)
- **PEFT adapters**: LoRA, DoRA, VeRA, LoRA+, TinyLoRA, OFT, HRA, WaveFT, LN-Tuning, VBLoRA, C3A
- **LoRA initializers**: PiSSA, EVA, CorDA, LoftQ for improved convergence
- **Multi-adapter PEFT**: multiple named adapters on one base model, switchable at runtime; merge with TIES, DARE, SVD, magnitude pruning